Loan default risk is a significant issue for financial institutions across the globe. It directly affects the profitability and financial soundness of financial institutions. The timely and accurate detection of potential defaulters is essential for efficient credit risk management. This paper presents an intelligent credit risk forecasting system that combines conventional machine learning classifiers with the most recent advancements in deep learning models. The data set includes past credit, financial, and demographic information gathered from previous loan applications. Various preprocessing methods, feature engineering solutions, and class imbalance problems solved using the Synthetic Minority Over-sampling Technique.(SMOTE) are used as techniques to enhance the quality of the data as well as the robustness of the models. Various predictive models, such as Logistic Regression, Random Forest, XGBoost, and the TabNet deep learning model, are compared. In addition, ensemble learning techniques are also used to mitigate misclassification and improve the generalization of the models. The experimental outcome shows that the TabNet and XGBoost models have the highest recall and accuracy in predicting default instances, thus minimizing the occurrence of false negatives. The hybrid model combines the interpretability of traditional machine learning models with the representation learning ability of deep learning models, providing a robust solution for real-time credit risk evaluation in the current banking system.
This paper proposed a data-driven approach to loan defaults risk prediction, using a combination of traditional machine learning, ensemble methods, and a deep learning model called TabNet. The experimental investigation showed that a model that can deal with non-linear interactions between features and also perform adaptive feature selection could yield better results than a baseline classifier typically applied in credit risk modeling [1], [3].
The inclusion of class balancing methods as well as the preprocessing of the data resulted in better results in classifying the default cases, which is a major problem with financial data sets as noted in [5], [6]. In particular, there was a higher recall rate for the default cases using the TabNet model with the cost-sensitive optimization methodology, indicating the suitability of such models for learning from structured data using attention-based mechanisms, as noted in [7].
Despite the powerful empirical performance of our proposed framework, there are certain limitations. The model is based on a single historical dataset and does not incorporate temporal borrower behavior or macroeconomic indicators, which have been seen to drive credit risk dynamics [2], [10]. Additionally, the static formulation of the default prediction task may not fully capture shifting borrower risk profiles over time and is, therefore, susceptible to generalization in real-world lending environments.
Such limitations can be addressed by conducting further research through the incorporation of time-series financial information, borrower behavior patterns, and other external economic indicators to ensure robustness and adaptability in such a framework as suggested by reference [6]. In conclusion, therefore, the current study clearly depicts the potential use of interpretable deep-learning-based models in the conduct of reliable and transparent credit risk assessment activities within the modern financial world as suggested by references [7], [8].
References
[1]Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing by Muhammad Asif Zahoor et al. (IEEE Access 2024)
[2]Design of Personal Credit Risk Prediction Model and Financial Risk Legal Prevention(IEEE Access 2024)
[3]Improved ADASYN Sampling and Optimized LightGBM for Credit Risk Prediction, Journal of Social Computing 2024
[4]Towards a Machine Learning-based Model for Corporate Loan Default Prediction IJACSA 2024
[5]Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets (IEEE Access, 2021)
[6]A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning (IEEE Access 2025)
[7]A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM (IEEE Access, 2023)
[8]AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector (IEEE Access, 2023)
Indicator of collective Insights(IEEE Access, 2021)
[9]Formal Specification and Verification of Smart Contract-Based Loan Management System Using TLA+(IEEE ACCESS,2025)
[10]Crowd Dynamics in Online Lending:Unveiling
How to Cite This Paper
Abhijit Kolekar, Rawal Awale, Panchakshari Awaje, Madhuri Tayade (2025). A Machine Learning and Deep Learning Approach to Predicting Loan Default Through Credit Risk Analysis. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
